Ultrasound Three-Dimensional (3-D) Segmentation

ABSTRACT

An ultrasound imaging system includes a beamformer configured to generate 2-D images offset from each other based on sweeping motion of a transducer array during a 3-D ultrasound imaging procedure producing volumetric data. The ultrasound imaging system further includes a 2-D mask processor configured to generate a 2-D mask image for each of the 2-D images. Each of the 2-D mask images includes a contour of a predetermined structure of interest in the volumetric data. The ultrasound imaging system further includes a 3-D mask processor configured to segment the structure from the 3-D image with the 2-D mask images, generating a 3-D segmentation.

TECHNICAL FIELD

The following generally relates to ultrasound and more particularly toultrasound three-dimensional (3-D) segmentation.

BACKGROUND

Medical ultrasound images are difficult to understand, e.g., because theultrasound image is typically in an oblique view compared to the naturalaxis of an organ as well as the natural axis of the body. A consequenceof the large variety of appearances of ultrasound images is that it canbe difficult for humans and machines to learn to recognize, segment andlabel their content. Furthermore, it is often difficult to distinguishbetween fluid and attenuated tissue in an ultrasound image and thisaffects automatic gain control.

Another shortcoming is color flow imaging outside vessels where the datastems from tissue movement or mirroring in arterial vessel walls.

SUMMARY

Aspects of the application address the above matters, and others.

In one aspect, an ultrasound imaging system includes a beamformerconfigured to generate 2-D images offset from each other based onsweeping motion of a transducer array during a 3-D ultrasound imagingprocedure producing volumetric data, a 2-D mask processor configured togenerate a 2-D mask image for each of the 2-D images, wherein each ofthe 2-D mask images includes a contour of a predetermined structure ofinterest in the volumetric data, and a 3-D mask processor configured tosegment the structure from the 3-D image with the 2-D mask images,generating a 3-D segmentation.

In another aspect, a method includes beamforming 2-D images offset fromeach other based on sweeping motion of a transducer array during a 3-Dultrasound imaging procedure producing volumetric data, generating a 2-Dmask image for each of the 2-D images, wherein each of the 2-D maskimages includes a contour of a predetermined structure of interest inthe volumetric data, and segmenting the structure from the 3-D imagewith the 2-D mask images.

In yet another aspect, a computer-readable storage medium storinginstructions that when executed by a computer cause the computer toperform a method for using a computer system to segment ultrasoundimaging data, the method comprising: beamforming 2-D images offset fromeach other based on sweeping motion of a transducer array during a 3-Dultrasound imaging procedure producing volumetric data, generating a 2-Dmask image for each of the 2-D images, wherein each of the 2-D maskimages includes a contour of a predetermined structure of interest inthe volumetric data, and segmenting the structure from the 3-D imagewith the 2-D mask images.

Those skilled in the art will recognize still other aspects of thepresent application upon reading and understanding the attacheddescription.

BRIEF DESCRIPTION OF THE DRAWINGS

The application is illustrated by way of example and not limited by thefigures of the accompanying drawings, in which like references indicatesimilar elements and in which:

FIG. 1 schematically illustrates an example ultrasound system configuredto perform a 3-D ultrasound image segmentation in accordance with anembodiment(s) described herein;

FIG. 2 illustrates an example ultrasound probe of the system of FIG. 1in accordance with an embodiment(s) described herein;

FIG. 3 illustrates an example frame of reference for the ultrasoundprobe of FIG. 2 in accordance with an embodiment(s) described herein;

FIG. 4 illustrates an example approach for re-slicing a 3-D image intoequiangular 2-D images in accordance with an embodiment(s) describedherein;

FIG. 5 illustrates an example of a plurality of re-sliced 2-D images inaccordance with an embodiment(s) described herein;

FIG. 6 illustrates an example of a plurality 2-D image maskscorresponding to the re-sliced 2-D images of FIG. 5 in accordance withan embodiment(s) described herein;

FIG. 7 illustrates an example 2-D image with a corresponding 2-D maskimage overlaid thereover in accordance with an embodiment(s) describedherein;

FIG. 8 schematically illustrates a variation of the embodiment of FIG. 1in accordance with an embodiment(s) described herein;

FIG. 9 schematically illustrates another variation of the embodiment ofFIG. 1 in accordance with an embodiment(s) described herein;

FIG. 10 schematically illustrates yet another variation of theembodiment of FIG. 1 in accordance with an embodiment(s) describedherein;

FIG. 11 schematically illustrates still another variation of theembodiment of FIG. 1 in accordance with an embodiment(s) describedherein; and

FIG. 12 illustrates an example method in accordance with anembodiment(s) described herein.

DETAILED DESCRIPTION

The following generally describes a 3-D segmentation approach forultrasound imaging. It also generally describes examples of utilizingthe 3-D segmentation, e.g., to adjust image acquisition, visualpresentation, etc.

FIG. 1 illustrates an example ultrasound imaging system 102. Theultrasound imaging system 102 includes a probe 104 and an ultrasoundconsole 106, which interface with each other through suitablecomplementary hardware (e.g., cable connectors and a cable, etc.) and/orwireless interfaces (not visible).

The probe 104 includes a transducer array 108 with one or moretransducer elements 110. The transducer array 108 can be 1-D or 2-D,linear, curved, and/or otherwise shaped, fully populated, sparse and/ora combination thereof, etc. array. A suitable probe includes anendocavitary and/or other probe.

In one instance, a user rotates the transducer array 108 to acquire aseries of 2-D images that are then combined to produce 3-D volumetricdata. FIG. 2 illustrates an example of such a probe, which includes ahandle 202, a shaft 204, a head 206, the array 108 behind an acousticwindow 210 and configured to provide a sagittal field of view (FOV) 212.Example rotation is shown at 214. The probe 104 of course can be rotatedin the opposite direction and/or translated. Another example probe isdescribed in U.S. Pat. No. 9,259,208 B1, filed Oct. 20, 2009, andentitled “Ultrasound Probe,” which is incorporated herein by referencein its entirety. Other probes are also contemplated. In anotherinstance, the transducer array 108 is configured to mechanically and/orelectrically rotate within the probe 104 to acquire such 2-D images.Non-limiting examples of such a probe is described in U.S. Pat. No.9,289,187 B2, titled “Imaging Transducer Probe,” and filed Jun. 10,2013, which is incorporated herein by reference in its entirety, and US2018/0185008 A1, titled “US Imaging Probe with an US Transducer Arrayand an Optical Imaging Subsystem,” and filed Dec. 19, 2017, which isincorporated herein by reference in its entirety. Other probes are alsocontemplated herein.

Returning to FIG. 1, the one or more transducer elements 110 areconfigured to convert an excitation electrical signal to an ultrasoundpressure field. The one or more transducer elements 110 are alsoconfigured to convert a received ultrasound pressure field (an echo)into an electrical (e.g., analog radio frequency, RF) signal. Thereceived ultrasound pressure field, in one instance, is produced inresponse to a transmitted ultrasound pressure field interacting withstructure, e.g., a prostate a bladder, a vessel, and/or other tissue.

The probe 104 further includes a probe tracking device 112. In oneinstance, the probe tracking device 112 is internal to the probe 104. Inanother instance, the probe tracking device 112 is external to the probe104. In yet another instance, the probe tracking device 112 is partiallyinternal and partially external to the probe 104. Examples of suitableprobe tracking devices include, but are not limited to inertial,absolute, motorized, optical, magnetic, etc.

An example inertial tracking device includes an accelerometer, agyroscope and/or a magnetometer and generate signals indicative of anorientation and/or a velocity. An example optical tracking deviceincludes elements affixed to a handle of the probe 104 and tracked viaan optical video camera. An example magnetic tracking device includescoils on the probe 104 and calculates position and orientation by arelative magnetic flux of the coils. Non-limiting example of suitabletracking devices are discussed in Birkfellner et al., “TrackingDevices,” In: Peters T., Cleary K. (eds) Image-Guided Interventions.Springer, Boston, Mass., 2008.

The console 106 includes transmit and receive circuitry (TX/RX) 114configured to generate the excitation signal conveyed to the transducerarray 108 for at least 3-D imaging by manual and/orelectrical-mechanical sweeping of the transducer array 108. The TX/RX114 is also configured to process the electrical signal corresponding tothe received echo signal. The TX/RX 114, in one instance, is furtherconfigured to pre-condition and/or pre-process the signal (e.g.,amplify, digitize, etc.). Other processing is also contemplated herein.

The illustrated embodiment shows the transmit and receive operations areperformed by the same circuitry, the TX/RX 114. In a variation, thetransmit and receive operations are performed by separate circuitry,e.g., transmit circuitry for transmit operations and separate receivecircuitry for receive operations. One or more switches and/or otherdevice(s) can be used to switch between transmit and receive operationsand/or transmit and receive circuitry by electrically connecting andelectrically disconnecting transmit and receive circuitry.

The console 106 further includes a beamformer 116. In one instance, thebeamformer 116 is configured to beamform the signal, e.g., viadelay-and-sum beamforming and/or other beamforming. For B-mode imaging,this includes generating a sequence of focused, coherent echo samplesalong focused scanlines of a scanplane to produce a 2-D image. When theprobe 104 is rotated during image acquisition, the beamformer 116generates a series of angularly spaced 2-D images. The relative positionof each image with respect to each other is indicated by the signal fromthe tracking device 112.

The console 106 further includes a 3-D processor 118. The 3-D processor118 is configured to generate a 3-D image from the series of 2-D imagesgenerated while rotating the probe 104 during imaging based on thesignal from the tracking device 112. In one instance, the 3-D processor118 generates the 3-D image by interpolating between neighboring 2-Dimages.

The console 106 further includes a 2-D re-slice processor 120. In oneinstance, the 2-D re-slice processor 120 is configured to re-slice the3-D image into a predetermined number of 2-D slices/images. For example,in one instance the 2-D re-slice processor 120 generates thirty (30) 2-Dslices, each spaced six (6) degrees away from its neighboring 2-Dslice(s). In another instance, the 2-D re-slice processor 120 generatessixty (60) 2-D slices, each spaced three (3) degrees away from itsneighboring 2-D slice(s). Other numbers of slices and angular increments(e.g., from 1-10) are also contemplated herein. The number and/orangular increment may depend on the particular object being scanned, thecomplexity of the object, processing time constraints, etc. Offsetsother than angular are also contemplated herein. An example is thetranslatory motion along one side of the neck of an ultrasoundtransducer placed transversely on the neck to insonify a lobe of thethyroid. Another example is the translatory motion along the body axisof an ultrasound transducer being perpendicular to the axis of motioninsonifying the aorta.

In general, the user will center the transducer array at a middle of theobject during scanning. The 2-D re-slice processor 120 re-slices the 3-Dimage with an anatomical axis of interest. e.g., a rotational symmetryautomatically based on the location of the centered transducer arraywith respect to the object during scanning, an anatomical axis ofinterest automatically based on another body part and/or an anatomicalaxis of interest automatically based on the body. This is in starkcontrast to a system which requires a user to select a best possibleaxis in an attempt to optimize for symmetry. As such, the approachdescribed herein does not require user interaction and/or input toidentify the axis and is thus entirely automated. This may also reducethe demand for objects for training the algorithm and/or increaseaccuracy. In another embodiment, the 2-D re-slice processor 120 utilizesa user selected axis.

FIG. 3 show a frame of reference/coordinate system with the origin atthe geometrical center of the sagittal array. The user will typicallyhave the tissue of interest in the center of the sweep. The x-axispoints into the figure. The y-axis is defined as the line in thexy-plane that splits the total sweep angle in two equal parts. Thez-axis points in the direction of the shaft 204 of the probe 104. Thez-axis is normal to the xy-plane.

The yz-plane is the sagittal body plane. The coordinate (x,y,z) forms aright-hand coordinate system. In FIG. 4, the 2-D re-slice processor 120re-slices the 3-D image so that all N slices contain the y-axis and areperpendicular to the xz-plane and are spaced with equal angular distancemaking the angle between the slices it/N. FIG. 5 shows an example withninety (90) re-sliced 2-D images of a prostate.

Returning to FIG. 1, the console 106 further includes a 2-D maskprocessor 122. The 2-D mask processor 122 is configured to generate atleast a 2-D mask for each re-sliced 2-D image. In one instance, the 2-Dmask processor 122 utilizes a convolutional neural network forpredicting objects masks and bounding boxes to generate the 2-D masks.One instance of such a network is a modified Mask Region-ConvolutionalNeural Network (Mask R-CNN), where the modification includes outputtinga mask in a floating-point mathematical representation (e.g., float32with values between 1e⁻⁹ (0.000000001) and 1.000000000) instead of abinary (0 or 1) representation. In one instance, this allows forlimiting quantization error during the reconstruction of a 3-D mask froma set of 2-D masks relative to systems, which utilize a binarymathematical representation.

The Mask R-CNN algorithm is further discussed in detail in “MaskR-CNN,”He et al., 24 Jan. 2018. As discussed therein, the Mask R-CNN algorithmuses a two-stage procedure. In the second stage, the Mask R-CNNalgorithm classifies individual objects in an image and localizes eachwith a labeled bounding box, in parallel with generating a binary maskfor each object in a bounding box. The mask branch is a FullyConvolutional Network (FCN) applied to each region of interest,predicting a segmentation mask in a pixel-to-pixel manner. The MaskR-CNN algorithm also outputs a confidence metric or level (e.g., as apercentage) for each mask that indicates a confidence that the objectclassification is correct. FIG. 6 shows masks corresponding to there-sliced 2-D images of FIG. 5. In everything that follows, the modifiedMask R-CNN algorithm will be used for generating 2-D masks, but the 2-Dmask processor could be replaced by another neural network-basedalgorithm, which generates 2-D mask using prior information obtainedthrough training.

Returning to FIG. 1, the console 106 further includes a 2-D maskselection processor 124. The 2-D mask selection processor 124 isconfigured to select all or less than all (i.e. a subset) of there-sliced 2-D images to reconstruct. For this, the 2-D mask selectionprocessor 124 utilizes the confidence level computed and output by theMask R-CNN algorithm for each 2-D mask image. The confidence level,e.g., is a value from zero to one (0-1) and indicates a confidence thatan outline/perimeter of tissue of interest in a mask is the trueperimeter, wherein a higher value indicates a greater confidence.

In one instance, the 2-D mask selection processor 124 compares theconfidence level generated by the Mask R-CNN algorithm with apredetermined threshold (e.g., 70%). The 2-D mask selection processor124 selects 2-D masks with confidence levels that satisfy thepredetermined threshold as images to reconstruct. The threshold can be adefault, a user preference, adjustable, static, etc. Calcifications cancast shadows making it difficult or impossible to recognize the prostateoutline with high confidence in some 2-D masks. These 2-D masks areexcluded from the reconstruction of the 3-D mask. This may improve theaccuracy of the reconstruction relative to a system that just uses allof the 2-D masks.

Before using the Mask R-CNN algorithm, the Mask R-CNN algorithm istrained with “ground truth” data and training data. The “ground truth”data, e.g., is a 3-D segmentation of tissue of interest. In oneinterest, the 3-D segmentation is produced through a semi-automatedprocess that includes expert (e.g., clinician) involvement (e.g., freehand segmentation) and confirmation. In another instance, the 3-Dsegmentation is produced through a computer fully automated process withexpert confirmation. The training data includes images re-sliced fromthe “ground truth” 3-D data.

The console 106 further includes a 3-D mask processor 126. The 3-D maskprocessor 126 is configured to reconstruct a 3-D mask/segmentation ofthe tissue of interest only with the 2-D masks with confidence levelsthat satisfied the predetermined threshold. In one instance, the 3-Dmask processor 126 reconstructs the 3-D mask by interpolating betweenneighboring 2-D masks. The 3-D mask processor 126 computes a volumemeasurement of the tissue based on the 3-D mask, which is a numericalquantity of a 3-D space enclosed by an external surface of the 3-Dmask/segmentation. In another instance, the volume measurement iscomputed without generating a 3-D mask, e.g., by adding r*\delta \theta,for each pixel in the 2-D masks of all the slices, where r denotes thedistance from a mask pixel to the approximate axis of rotation and\delta \theta denotes the angle between the previous and the next goodimage plane for the image to which the current mask pixel belongs.

The console 106 further includes a display 128. In one instance, animage of the reconstructed volume is displayed via the display 128 withthe corresponding 2-D mask image overlaid over the 2-D image. This mayallow the user to quickly verify that the segmentation is correct. FIG.7 shows an example of an image 702 including a prostate with a 2-D maskimage 704 overlaid over the prostate in the image 702. Besides thevolume measurement, a height, width and length can be computed by thesystem for the object that is outlined by the 3-D mask and be displayed.Alternatively, or additionally, user-adjustable height, width and lengthcaliper computer tools are placed in sagittal and transverse viewsallowing the user to calculate the prostate volume in a more traditionalway.

It is to be appreciated that one or more of the beamformer 116, the 3-Dprocessor, the 2-D re-slice processor 120, the 2-D mask processor 122,the 2-D mask selector 124 and/or the 3-D mask processor 126 isimplemented via a computer processor (e.g., central processing unit(CPU), microprocessor, etc.). For example, in one instance, one or moreof the beamformer 116, the 3-D processor, the 2-D re-slice processor120, the 2-D mask processor 122, the 2-D mask selector 124 and/or the3-D mask processor 126 are implemented by way of processor executingcomputer readable instructions, encoded or embedded on computer readablestorage medium (which excludes transitory medium).

The automated 3-D segmentation approach described herein mitigateshaving a user define the axis of rotation, which reduces overallprocessing time relative to a system in which the user defines the axisof rotation and thus is an improvement to an existing technologicalprocess. However, the automated 3-D segmentation approach describedherein is not just an automation of an existing non-computerized processin a same manner. Furthermore, 2-D mask images that do not produce ahighly confident contours are not used to segment the structure in 3-Dor compute the volume measurement, mitigating error introduced fromcalcifications, etc. obscuring visibility in parts of the structure ofinterest and thus improves accuracy. Furthermore, the approach describedherein allows a user to determine an acceptable fidelity of thesegmentation.

In a variation, the 2-D re-slice processor 120 is omitted or bypassedand the 2-D mask processor 122 generates the 2-D masks with thebeamformed 2-D images.

The approach described herein is well-suited for segmenting 3-D tissuesuch as a prostate, a bladder, a vessel, etc. The 3-D segmentation canbe used to adjust visual presentation, image acquisition and/or otherparameters. The following provides some non-liming examples.

In general, automatic time gain control (TGC) algorithms try tocompensate for the difference between the average soft tissueattenuation of 0.5 dB/(cm MHz) and the actual attenuation, which may bemuch lower due to fluids attenuate the signals much less. Otherphenomena which affect the image brightness include uneven transducercontact, rib shadows, and reflecting interfaces. In order to deliver auniform image to the user, the above is taken into account and the 3-Dsegmentation approach described herein provides information foradjusting the gain properly.

For example, the approach described herein improves the ability todistinguish between fluid and attenuated tissue in an ultrasound imagefor TGC. In one instance, a TGC controller 802 (FIG. 8) employs theconfidence level to reduce the weight of sample values inside likelyfluid collections, regardless of whether these collections also haveweak tissue signals. By doing so, temporal effects of fluctuating imagebrightness based on whether or not a particular area of the image wereidentified as being a fluid collection or not are mitigated. Thismitigates instances where it is difficult to distinguish between fluidand attenuated tissue in an ultrasound image.

A non-limiting example of a suitable time gain compensation is describedin international application publication number WO2017/122042A1, titled“Automatic Time Gain Compensation (TGC) in Ultrasound Imaging,” andfiled Jul. 10, 2018, which is incorporated herein by reference in itsentirety. In the approach described in WO2017/122042A1, the confidencelevel can be used in conjunction with the TGC matrices to reduce theweight of sample values inside likely fluid collections, regardless ofwhether these collections also have weak tissue signals. The confidencelevel can also be employed with other TGC approaches.

In another example, the approach described herein is used to make avessel mask for removal of color flow outside vessels, where the falsecolor flow may stem from tissue movement or may be a result of mirroringin the vessel wall itself such as the carotid artery. With the 3-Dsegmentation described herein, organs are delineated properly, and it ispossible to erase unwanted color flow. For instance, a color flowprocessor 902 (FIG. 9) employs the vessel mask to suppress color signalsoutside of this mask, which mitigates the problem is color flow imagingoutside vessels where the data stems from tissue movement or mirroringin arterial vessel walls.

In another example, the approach described herein allows for modifyingimage acquisition. For example, where an image includes both the liverand a kidney, which are identified by the detection algorithm, the usercan select an organ of interest (the liver or the kidney) by actuating acontrol that invokes a parameter selector 1002 (FIG. 10) toautomatically select parameters such as depth, frequency, color boxposition, pulse repetition frequency, color parameters, etc.Furthermore, a labeler/annotator selector 1102 (FIG. 11) labels and/orannotates tissue to spatially identify tissue, such as the kidneys,which include a pair (e.g., left and right) of structures.

In another example, the approach described herein can be used to assistsurgery. For example, a 3-D ultrasound volume of part of the brain canbe created during surgery to assist locating brain structures that wereshifted once the skull was opened. Segmentation of a potential tumor aswell as identification and segmentation of important structures thatneed to be preserved will be important to the success of the surgery.

In another embodiment, two or more of the embodiments described in FIGS.8, 9, 10 and 11 are combined together.

FIG. 12 illustrates an example method in accordance with an embodimentherein.

At 1200, the transducer elements 110 are swept over tissue of interestto acquire data.

In one instance, this includes first locating the tissue of interestsuch that the entire tissue of interest is displayed. This may entailcentering the transducer array 108 on the central axis of the tissue ofinterest. Then, the probe 104 is rotated in one direction until thetissue of interest is no longer in the field of view. Then the probe 104is rotated in the opposite direction while scanning until again thetissue of interest is no longer in the field of view to capture data forthe entire volume.

At 1202, the acquired data are processed to generate a series of 2-Dimages angularly offset from each other based on the sweep rotation, asdescribed herein and/or otherwise.

At 1204, the series of 2-D images are processed to generate a 3-D image,as described herein and/or otherwise.

At 1206, the 3-D image is processed to create a predetermined number ofre-sliced 2-D images separated by a same predetermined angular width, asdescribed herein and/or otherwise.

At 1208, 2-D mask images are generated with the re-sliced 2-D images.

At 1210, 2-D mask images with a predetermined contour confidence areidentified, as described herein and/or otherwise.

At 1212, the identified 2-D mask images are processed to segment tissueof interest in 3-D, as described herein and/or otherwise.

At 1214, a volume of the tissue of interest is computed based on the 3-Dsegmentation, as described herein and/or otherwise.

Optionally, a 2-D image is displayed with a corresponding 2-D mask imageoverlaid thereover.

The above may be implemented by way of computer readable instructions,encoded or embedded on computer readable storage medium (which excludestransitory medium), which, when executed by a computer processor(s)(e.g., central processing unit (CPU), microprocessor, etc.), cause theprocessor(s) to carry out acts described herein. Additionally, oralternatively, at least one of the computer readable instructions iscarried by a signal, carrier wave or other transitory medium, which isnot computer readable storage medium.

The application has been described with reference to variousembodiments. Modifications and alterations will occur to others uponreading the application. It is intended that the invention be construedas including all such modifications and alterations, including insofaras they come within the scope of the appended claims and the equivalentsthereof.

What is claimed is:
 1. An ultrasound imaging system, comprising: a beamformer configured to generate 2-D images offset from each other based on sweeping motion of a transducer array during a 3-D ultrasound imaging procedure producing volumetric data; a 2-D mask processor configured to generate a 2-D mask image for each of the 2-D images, wherein each of the 2-D mask images includes a contour of a predetermined structure of interest in the volumetric data; and a 3-D mask processor configured to segment the structure from the volumetric data with the 2-D mask images, generating a 3-D segmentation.
 2. The ultrasound imaging system of claim 1, further comprising: a display configured to display a 2-D image with a corresponding 2-D mask overlaid thereover.
 3. The ultrasound imaging system of claim 1, where the 3-D mask processor is further configured to compute a volume measurement of the structure from the 3-D segmentation.
 4. The ultrasound imaging system of claim 1, further comprising: a 3-D image processor configured to construct a 3-D image from the 2-D images; and a 2-D re-slice processor configured to generate a set of re-sliced 2-D images that follow an anatomical axis of interest from the 3-D image, wherein the 2-D mask processor is configured to generate a 2-D mask image for each re-sliced 2-D image.
 5. The ultrasound imaging system of claim 4, wherein the 2-D mask processor is configured to further generate a confidence level for each of the 2-D images, and further comprising: a 2-D mask selector configured to identify 2-D mask images with confidence levels that satisfy a predetermined confidence threshold, wherein the 3-D mask processor is configured to segment the structure from the 3-D image only with the identified 2-D mask images.
 6. The ultrasound imaging system of claim 1, further comprising: a display configured to display one or more 2-D images with software calipers to measure a height, a width and a length of tissue of interest.
 7. The ultrasound imaging system of claim 1, further comprising: a time gain control controller configured to control a gain of pixels of the 2-D images based on the 3-D segmentation.
 8. The ultrasound imaging system of claim 1, further comprising: a color flow processor configured to remove color flow outside of a vessel based on the 3-D segmentation.
 9. The ultrasound imaging system of claim 1, further comprising: a parameter selector configured to automatically select acquisition or visualization parameters based on a user input selecting tissue of interest where the 2-D mask processor identifies more than one type of tissue in the 2-D images.
 10. The ultrasound imaging system of claim 1, further comprising: a labeler configured to label or annotate tissue to spatially identify tissue that includes a pair of or many structures.
 11. A method, comprising: beamforming 2-D images offset from each other based on sweeping motion of a transducer array during a 3-D ultrasound imaging procedure producing volumetric data; generating a 2-D mask image for each of the 2-D images, wherein each of the 2-D mask images includes a contour of a predetermined structure of interest in the volumetric data; and segmenting the structure from the 3-D image with the 2-D mask images.
 12. The method of claim 11, further comprising: computing a numerical quantity of a 3-D space enclosed by an external surface of the segmented structure.
 13. The method of claim 12, further comprising: constructing a 3-D image from the 2-D images; and generating a set of re-sliced 2-D images that follow an anatomical axis of interest from the 3-D image, wherein the 2-D mask images are generated for the set of re-sliced 2-D images.
 14. The method of claim 13, further comprising: generating a confidence level for each of the 2-D images; and identifying 2-D mask images with confidence levels that satisfy a predetermined confidence threshold, wherein the structure is segmented from the 3-D image only with the identified 2-D mask images.
 15. The method of claim 14, further comprising: displaying a 2-D image with a corresponding 2-D mask overlaid thereover.
 16. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for using a computer system to segment ultrasound imaging data, the method comprising: beamforming 2-D images offset from each other based on sweeping motion of a transducer array during a 3-D ultrasound imaging procedure producing volumetric data; generating a 2-D mask image for each of the 2-D images, wherein each of the 2-D mask images includes a contour of a predetermined structure of interest in the volumetric data; and segmenting the structure from the 3-D image with the 2-D mask images.
 17. The computer-readable storage medium of claim 16, the method further comprising: computing a volume of the structure from the segmented structure.
 18. The computer-readable storage medium of claim 17, the method further comprising: constructing a 3-D image from the 2-D images; and generating a set of re-sliced 2-D images that follow an anatomical axis of interest from the 3-D image, wherein the 2-D mask images are generated for the set of re-sliced 2-D images.
 19. The computer-readable storage medium of claim 18, the method further: generating a confidence level for each of the 2-D images; and identifying 2-D mask images with confidence levels that satisfy a predetermined confidence threshold, wherein the structure is segmented from the 3-D image only with the identified 2-D mask images.
 20. The computer-readable storage medium of claim 19, the method further: displaying a 2-D image with a corresponding 2-D mask overlaid thereover. 